{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/peremartra/Large-Language-Model-Notebooks-Course/blob/main/4-Evaluating%20LLMs/4_2_Evaluating_summaries_embeddings.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6c2f8fcc-0d7f-422b-8d23-6e2e70a7c4a5",
      "metadata": {
        "id": "6c2f8fcc-0d7f-422b-8d23-6e2e70a7c4a5"
      },
      "source": [
        "<div>\n",
        "    <h1>Large Language Models Projects</a></h1>\n",
        "    <h3>Apply and Implement Strategies for Large Language Models</h3>\n",
        "    <h2>4.2-Tracing and Evaluating LLMs with LangSmith.</h2>\n",
        "    <h3>Evaluating summaries using embedding distance</h3>\n",
        "</div>\n",
        "\n",
        "by [Pere Martra](https://www.linkedin.com/in/pere-martra/)\n",
        "\n",
        "__________\n",
        "Models: t5-base-cnn / t5-base / OpenAI\n",
        "\n",
        "Colab Environment: CPU\n",
        "\n",
        "Keys:\n",
        "* Embeddings\n",
        "* LangSmith\n",
        "\n",
        "Related article: [Evaluating LLM Summaries using Embedding Distance with LangSmith](https://pub.towardsai.net/evaluating-llm-summaries-using-embedding-distance-with-langsmith-5fb46fdae2a5?sk=24eb18ce187d28547cebd6fd3dd1ddad)\n",
        "\n",
        " If you want to run this notebook, you will need to have API keys from OpenAI, HuggingFace, and LangChain.\n",
        "_________\n",
        "\n",
        "Previously in the notebook, [4_1_rouge_evaluations.ipynb](https://github.com/peremartra/Large-Language-Model-Notebooks-Course/blob/main/4-Evaluating%20LLMs/4_1_rouge_evaluations.ipynb), we learned how to use ROUGE to evaluate which summary best approximated the one created by a human. This time, we will use embedding distance and LangSmith to verify which model produces summaries more similar to the reference ones.\n",
        "\n",
        "We will continue using the same dataset and models as in the example with ROUGE. That is, a dataset containing CNN news articles and their human-created summaries, which will be taken as the reference. Additionally, we have two T5 models, with one of them fine-tuned specifically for generating summaries.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a1a5ae21-8eaf-431c-bb2f-22a8dc8b7e47",
      "metadata": {
        "id": "a1a5ae21-8eaf-431c-bb2f-22a8dc8b7e47",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3f02f1ca-4bd0-4712-b30d-ec50090ad7de"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.4/50.4 kB\u001b[0m \u001b[31m1.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m47.1/47.1 kB\u001b[0m \u001b[31m597.0 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m337.0/337.0 kB\u001b[0m \u001b[31m8.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m23.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.6/75.6 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.9/77.9 kB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m547.8/547.8 kB\u001b[0m \u001b[31m18.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m8.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m316.1/316.1 kB\u001b[0m \u001b[31m18.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m39.9/39.9 MB\u001b[0m \u001b[31m18.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m64.9/64.9 kB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m7.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m11.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "torch 2.3.1+cu121 requires nvidia-cublas-cu12==12.1.3.1; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.1+cu121 requires nvidia-cuda-cupti-cu12==12.1.105; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.1+cu121 requires nvidia-cuda-nvrtc-cu12==12.1.105; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.1+cu121 requires nvidia-cuda-runtime-cu12==12.1.105; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.1+cu121 requires nvidia-cudnn-cu12==8.9.2.26; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.1+cu121 requires nvidia-cufft-cu12==11.0.2.54; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.1+cu121 requires nvidia-curand-cu12==10.3.2.106; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.1+cu121 requires nvidia-cusolver-cu12==11.4.5.107; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.1+cu121 requires nvidia-cusparse-cu12==12.1.0.106; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.1+cu121 requires nvidia-nccl-cu12==2.20.5; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.1+cu121 requires nvidia-nvtx-cu12==12.1.105; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.\n",
            "gcsfs 2024.6.1 requires fsspec==2024.6.1, but you have fsspec 2024.5.0 which is incompatible.\n",
            "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\n",
            "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 17.0.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m41.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.2/49.2 kB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "#Loading Necessary Libraries\n",
        "!pip install -q transformers==4.42.4\n",
        "!pip install -q langchain==0.2.11\n",
        "!pip install -q langchain-openai==0.1.19\n",
        "!pip install -q langchainhub==0.1.20\n",
        "!pip install -q datasets==2.20.0\n",
        "!pip install -q huggingface-hub==0.23.5\n",
        "!pip install -q langchain-community==0.2.10"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6db4887a-a214-49bc-9bb6-0ae59c1092ec",
      "metadata": {
        "id": "6db4887a-a214-49bc-9bb6-0ae59c1092ec",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "38cf4643-e39c-4e61-d393-c829803c3e32"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "LangChain API Key: ··········\n"
          ]
        }
      ],
      "source": [
        "#You need a LangChain API Key.\n",
        "from getpass import getpass\n",
        "import os\n",
        "if not 'LANGCHAIN_API_KEY' in os.environ:\n",
        "  os.environ[\"LANGCHAIN_API_KEY\"] = getpass(\"LangChain API Key: \")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ca8e338c-45e2-42d1-94f2-64b70cc4b529",
      "metadata": {
        "id": "ca8e338c-45e2-42d1-94f2-64b70cc4b529",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d1f92930-c13b-4cc3-e100-de05d08ee418"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "OPENAI API Key: ··········\n"
          ]
        }
      ],
      "source": [
        "if not 'OPENAI_API_KEY' in os.environ:\n",
        "  os.environ[\"OPENAI_API_KEY\"] = getpass(\"OPENAI API Key: \")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5f4adff9-1169-4173-a72b-c983dcb6160d",
      "metadata": {
        "id": "5f4adff9-1169-4173-a72b-c983dcb6160d"
      },
      "outputs": [],
      "source": [
        "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
        "os.environ[\"LANGCHAIN_ENDPOINT\"]=\"https://api.smith.langchain.com\"\n",
        "#os.environ[\"LANGCHAIN_PROJECT\"]=\"langsmith_yttest01\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0189af0e-8145-407b-a32d-2f21ac0ca5ff",
      "metadata": {
        "id": "0189af0e-8145-407b-a32d-2f21ac0ca5ff"
      },
      "outputs": [],
      "source": [
        "#Importing Client from Langsmith\n",
        "from langsmith import Client\n",
        "client = Client()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4e7c4be2-fdae-42cf-b1b2-9aef89081ad2",
      "metadata": {
        "id": "4e7c4be2-fdae-42cf-b1b2-9aef89081ad2"
      },
      "source": [
        "## Create Dataset\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dbc219ae-1760-4f3d-92e6-aaeb6fb7ee9f",
      "metadata": {
        "id": "dbc219ae-1760-4f3d-92e6-aaeb6fb7ee9f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337,
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          ]
        },
        "outputId": "b264f671-4b7a-445e-bb08-0355ca2d2a59"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/9.27k [00:00<?, ?B/s]"
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        },
        {
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          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/13.9k [00:00<?, ?B/s]"
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        {
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              "model_id": "cca25e066a27489b8e887d4edf3da5e7"
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          "metadata": {}
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        {
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              "model_id": "eed06816864e4037add989a7d1bbb26c"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading data:   0%|          | 0.00/12.3M [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "f6b682f4a24a4339bf0d0eefca6f3c02"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading data:   0%|          | 0.00/661k [00:00<?, ?B/s]"
            ],
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              "model_id": "6be079a960fc4367a855fa1f98ee51bc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bcfb661854964057b0de85ea0a0bcd8b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7de9bac365ba40d8b578127aa174ea48"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4d0e6502cf5b411fa89352b1f47ffafd"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "cnn_dataset = load_dataset(\n",
        "    \"ccdv/cnn_dailymail\", version\n",
        "    =\"3.0.0\",\n",
        "    trust_remote_code=True\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7684f9ef-f665-42c1-ac89-f2726f7ae0da",
      "metadata": {
        "id": "7684f9ef-f665-42c1-ac89-f2726f7ae0da"
      },
      "outputs": [],
      "source": [
        "def add_prefix(example):\n",
        "    return {\n",
        "        **example,\n",
        "        \"article\": f\"Summarize this news:\\n{example['article']}\"\n",
        "    }\n",
        "\n",
        "#cnn_dataset = cnn_dataset.map(add_prefix)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3fc26871-68d9-4ab1-a9c9-b6caf241d636",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118,
          "referenced_widgets": [
            "4cf23aa63965479fa553e146c03717b3",
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            "23a2420ec9d144aab6a0609b3cdb0fa4",
            "bc0036ed277244f58931aa86b674cb92"
          ]
        },
        "id": "3fc26871-68d9-4ab1-a9c9-b6caf241d636",
        "outputId": "fea8c9f8-860b-4bff-8214-8c240f59a38b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/3 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4cf23aa63965479fa553e146c03717b3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['article', 'highlights', 'id'],\n",
              "    num_rows: 3\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "#Get just a few news to test\n",
        "MAX_NEWS=3\n",
        "sample_cnn = cnn_dataset[\"test\"].select(range(MAX_NEWS)).map(add_prefix)\n",
        "\n",
        "sample_cnn"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bc0ef576-ea25-446d-a99b-354678a274ba",
      "metadata": {
        "id": "bc0ef576-ea25-446d-a99b-354678a274ba"
      },
      "source": [
        "The dataset contains three columns: article, highlights, and id. To use LangSmith, we need to create a dataset in LangSmith format.\n",
        "\n",
        "LangSmith expects a prompt and a result. To achieve this, we will transform the article into a prompt by adding the prefix: \"Summarize this news.\" As a result, we will use the content of highlights, which represents the summaries created by humans."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4e3cea3a-fde5-4e52-90f9-d1db4fd30bc2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4e3cea3a-fde5-4e52-90f9-d1db4fd30bc2",
        "outputId": "4871009e-8d4b-401a-a887-87f4410ca568"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'article': 'Summarize this news:\\n(CNN)James Best, best known for his portrayal of bumbling sheriff Rosco P. Coltrane on TV\\'s \"The Dukes of Hazzard,\" died Monday after a brief illness. He was 88. Best died in hospice in Hickory, North Carolina, of complications from pneumonia, said Steve Latshaw, a longtime friend and Hollywood colleague. Although he\\'d been a busy actor for decades in theater and in Hollywood, Best didn\\'t become famous until 1979, when \"The Dukes of Hazzard\\'s\" cornpone charms began beaming into millions of American homes almost every Friday night. For seven seasons, Best\\'s Rosco P. Coltrane chased the moonshine-running Duke boys back and forth across the back roads of fictitious Hazzard County, Georgia, although his \"hot pursuit\" usually ended with him crashing his patrol car. Although Rosco was slow-witted and corrupt, Best gave him a childlike enthusiasm that got laughs and made him endearing. His character became known for his distinctive \"kew-kew-kew\" chuckle and for goofy catchphrases such as \"cuff \\'em and stuff \\'em!\" upon making an arrest. Among the most popular shows on TV in the early \\'80s, \"The Dukes of Hazzard\" ran until 1985 and spawned TV movies, an animated series and video games. Several of Best\\'s \"Hazzard\" co-stars paid tribute to the late actor on social media. \"I laughed and learned more from Jimmie in one hour than from anyone else in a whole year,\" co-star John Schneider, who played Bo Duke, said on Twitter. \"Give Uncle Jesse my love when you see him dear friend.\" \"Jimmy Best was the most constantly creative person I have ever known,\" said Ben Jones, who played mechanic Cooter on the show, in a Facebook post. \"Every minute of his long life was spent acting, writing, producing, painting, teaching, fishing, or involved in another of his life\\'s many passions.\" Born Jewel Guy on July 26, 1926, in Powderly, Kentucky, Best was orphaned at 3 and adopted by Armen and Essa Best, who renamed him James and raised him in rural Indiana. Best served in the Army during World War II before launching his acting career. In the 1950s and 1960s, he accumulated scores of credits, playing a range of colorful supporting characters in such TV shows as \"The Twilight Zone,\" \"Bonanza,\" \"The Andy Griffith Show\" and \"Gunsmoke.\" He later appeared in a handful of Burt Reynolds\\' movies, including \"Hooper\" and \"The End.\" But Best will always be best known for his \"Hazzard\" role, which lives on in reruns. \"Jimmie was my teacher, mentor, close friend and collaborator for 26 years,\" Latshaw said. \"I directed two of his feature films, including the recent \\'Return of the Killer Shrews,\\' a sequel he co-wrote and was quite proud of as he had made the first one more than 50 years earlier.\" People we\\'ve lost in 2015 . CNN\\'s Stella Chan contributed to this story.', 'highlights': 'James Best, who played the sheriff on \"The Dukes of Hazzard,\" died Monday at 88 .\\n\"Hazzard\" ran from 1979 to 1985 and was among the most popular shows on TV .', 'id': '00200e794fa41d3f7ce92cbf43e9fd4cd652bb09'}\n"
          ]
        }
      ],
      "source": [
        "print(sample_cnn[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1051e780-8871-4fd4-9e18-68ad8bc837f8",
      "metadata": {
        "id": "1051e780-8871-4fd4-9e18-68ad8bc837f8"
      },
      "source": [
        "Now We have the Dataset with the prompt and the Reference Summary, it is time to create a Dataset in LangSmith with this information.\n",
        "### Create the Dataset in Langsmith\n",
        "\n",
        "The dataset in LangSmith is composed of an input, which is the prompt passed to the model for evaluation, and an output, which should contain what we expect the model to return."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "221d8e92-c2f1-499c-9e68-3abb752722c4",
      "metadata": {
        "id": "221d8e92-c2f1-499c-9e68-3abb752722c4"
      },
      "outputs": [],
      "source": [
        "#import uuid\n",
        "import datetime\n",
        "input_key=['article']\n",
        "output_key=['highlights']\n",
        "\n",
        "NAME_DATASET=f\"Summarize_dataset_{datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dcf23c41-e15d-4f63-bd27-233d6599e305",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "397ef2a3df3e46af855a15df5b6ec441",
            "76b9f69dd4fb4122b9e572a42e0ff868",
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            "e28ac752d68a4e72ae98437f01abd538",
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            "fc2b7e32dd0446da9caf7e65b8f0c609",
            "8d4be0ada2754502a0dd09a94408c552",
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        "outputId": "83287014-9036-4649-be84-76b521a1ebe1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Creating CSV from Arrow format:   0%|          | 0/1 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "397ef2a3df3e46af855a15df5b6ec441"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "#This create the Dataset in LangSmith with the content in sample_cnn\n",
        "dataset = client.upload_dataframe(\n",
        "    df=sample_cnn,\n",
        "    input_keys=input_key,\n",
        "    output_keys=output_key,\n",
        "    name=NAME_DATASET,\n",
        "    description=\"Test Embedding distance between model summarizations\",\n",
        "    data_type=\"kv\"\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ada2bd55-b6bc-467c-b481-48d46ad50441",
      "metadata": {
        "id": "ada2bd55-b6bc-467c-b481-48d46ad50441"
      },
      "source": [
        "In this image, we can see an example from the dataset once it's been registered in LangSmith.\n",
        "\n",
        "In the Input column, there is the prompt to be sent, while in the Output column, the expected output is stored.\n",
        "\n",
        "When performing the comparison, the model will be given the prompt, and the Cosine distance between its response and the one stored in the sample dataset will be calculated.\n",
        "<img src=\"https://github.com/peremartra/Large-Language-Model-Notebooks-Course/blob/main/img/Martra_Figure_4_2SDL_Dataset.jpg?raw=true\">"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8278f53f-1a94-407c-846d-05c926737704",
      "metadata": {
        "id": "8278f53f-1a94-407c-846d-05c926737704"
      },
      "source": [
        "### Recovering Models From Hugging Face\n",
        "Let's retrieve both models from HuggingFace. A base T5 model and a model that has been fine-tuned using the training portion of this same dataset to generate summaries."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0f1afd20-0bbe-473b-9ad9-ebceb3519421",
      "metadata": {
        "id": "0f1afd20-0bbe-473b-9ad9-ebceb3519421"
      },
      "outputs": [],
      "source": [
        "from langchain import HuggingFaceHub"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5213c43f-85e2-4159-8a8e-684e79850bae",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5213c43f-85e2-4159-8a8e-684e79850bae",
        "outputId": "4576968c-3c9d-4f3f-c580-07f9e6e1fe05"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Hugging Face Key: ··········\n"
          ]
        }
      ],
      "source": [
        "from getpass import getpass\n",
        "hf_key = getpass(\"Hugging Face Key: \")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "12339bcc-ce70-4fae-9dcb-f2f273429ed8",
      "metadata": {
        "id": "12339bcc-ce70-4fae-9dcb-f2f273429ed8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7d035e2f-2870-4556-81a8-f93e5cbe9fee"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py:139: LangChainDeprecationWarning: The class `HuggingFaceHub` was deprecated in LangChain 0.0.21 and will be removed in 0.3.0. An updated version of the class exists in the langchain-huggingface package and should be used instead. To use it run `pip install -U langchain-huggingface` and import as `from langchain_huggingface import HuggingFaceEndpoint`.\n",
            "  warn_deprecated(\n"
          ]
        }
      ],
      "source": [
        "summarizer_base = HuggingFaceHub(\n",
        "    repo_id=\"t5-base\",\n",
        "    model_kwargs={\"temperature\":0, \"max_length\":180},\n",
        "    huggingfacehub_api_token=hf_key\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f317d1cc-95f0-4f71-9c7f-205e42afc5df",
      "metadata": {
        "id": "f317d1cc-95f0-4f71-9c7f-205e42afc5df"
      },
      "outputs": [],
      "source": [
        "summarizer_finetuned = HuggingFaceHub(\n",
        "    repo_id=\"flax-community/t5-base-cnn-dm\",\n",
        "    model_kwargs={\"temperature\":0, \"max_length\":180},\n",
        "    huggingfacehub_api_token=hf_key\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "dc859325-3e92-4f2b-85cc-247dd84d9cf6",
      "metadata": {
        "id": "dc859325-3e92-4f2b-85cc-247dd84d9cf6"
      },
      "source": [
        "## Defining Evaluator\n",
        "The first step is to define an evaluator, where we specify the variables we want to evaluate. In our case, I have chosen to measure only the \"embedding_distance.\"\n",
        "\n",
        "I've left the \"string_distance\" as a comment in case you want to conduct a test with two evaluations instead of one.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d506d770-2c30-41a6-acde-283ad1b27ea9",
      "metadata": {
        "id": "d506d770-2c30-41a6-acde-283ad1b27ea9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "06773952-3bb1-48b2-a2ce-a60f3e034866"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/3.4 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m \u001b[32m2.2/3.4 MB\u001b[0m \u001b[31m67.5 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m3.4/3.4 MB\u001b[0m \u001b[31m72.7 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.4/3.4 MB\u001b[0m \u001b[31m44.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "from langchain.smith import run_on_dataset, RunEvalConfig\n",
        "!pip install -q rapidfuzz==3.6.1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "15e8accd-d7e1-44f8-b5d0-0215ceedb88d",
      "metadata": {
        "id": "15e8accd-d7e1-44f8-b5d0-0215ceedb88d"
      },
      "outputs": [],
      "source": [
        "#We are using just one of the multiple evaluator available on LangSmith.\n",
        "evaluation_config = RunEvalConfig(\n",
        "    evaluators=[\n",
        "        \"embedding_distance\",\n",
        "        #RunEvalConfig.Criteria(\"conciseness\"),\n",
        "        #RunEvalConfig.Criteria(\"harmfulness\"),\n",
        "        #\"string_distance\"\n",
        "    ],\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ee666c30-d958-45b1-802c-7034dd13b42b",
      "metadata": {
        "id": "ee666c30-d958-45b1-802c-7034dd13b42b"
      },
      "source": [
        "## Running Evaluator\n",
        "With the same configuration, we can launch two evaluations on the same dataset. One for each of the chosen models."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "chain_models = [summarizer_base, summarizer_finetuned]"
      ],
      "metadata": {
        "id": "EnrXICCZSVy1"
      },
      "id": "EnrXICCZSVy1",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "67c75c07-8351-465f-af15-a1abe17968f9",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "67c75c07-8351-465f-af15-a1abe17968f9",
        "outputId": "701c857b-8bbd-4fa0-f9e2-0384f64eb18d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "View the evaluation results for project 'T5-BASE 2024-07-28 13:57:36' at:\n",
            "https://smith.langchain.com/o/c67fe7fc-f385-519d-9a10-9464d2be3d9d/datasets/951ef736-0cb1-4d58-a4e2-d41da4d7b758/compare?selectedSessions=e1d9e763-3025-4324-bbc2-24b041d67f02\n",
            "\n",
            "View all tests for Dataset Summarize_dataset_2024-07-28 13:57:02 at:\n",
            "https://smith.langchain.com/o/c67fe7fc-f385-519d-9a10-9464d2be3d9d/datasets/951ef736-0cb1-4d58-a4e2-d41da4d7b758\n",
            "[------------------------------------------------->] 3/3"
          ]
        }
      ],
      "source": [
        "project_name = f\"T5-BASE {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\"\n",
        "\n",
        "base_t5_results = run_on_dataset(\n",
        "    client=client,\n",
        "    project_name=project_name,\n",
        "    dataset_name=NAME_DATASET,\n",
        "    llm_or_chain_factory=summarizer_base,\n",
        "    evaluation=evaluation_config,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "76ede4d6-ed51-4745-b91a-d9ec6c40d436",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "76ede4d6-ed51-4745-b91a-d9ec6c40d436",
        "outputId": "47e4e807-f163-4a90-e24b-aeee989b5197"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "View the evaluation results for project 'T5-FineTuned 2024-07-28 13:57:44' at:\n",
            "https://smith.langchain.com/o/c67fe7fc-f385-519d-9a10-9464d2be3d9d/datasets/951ef736-0cb1-4d58-a4e2-d41da4d7b758/compare?selectedSessions=dac25105-a6c5-48fb-ac47-8d6a0921c2e2\n",
            "\n",
            "View all tests for Dataset Summarize_dataset_2024-07-28 13:57:02 at:\n",
            "https://smith.langchain.com/o/c67fe7fc-f385-519d-9a10-9464d2be3d9d/datasets/951ef736-0cb1-4d58-a4e2-d41da4d7b758\n",
            "[------------------------------------------------->] 3/3"
          ]
        }
      ],
      "source": [
        "project_name = f\"T5-FineTuned {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\"\n",
        "\n",
        "finetuned_t5_results = run_on_dataset(\n",
        "    client=client,\n",
        "    project_name=project_name,\n",
        "    dataset_name=NAME_DATASET,\n",
        "    llm_or_chain_factory=summarizer_finetuned,\n",
        "    evaluation=evaluation_config,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "953211fe-b8a4-4a2c-91e3-3c6242a2f159",
      "metadata": {
        "id": "953211fe-b8a4-4a2c-91e3-3c6242a2f159"
      },
      "source": [
        "<img src=\"https://github.com/peremartra/Large-Language-Model-Notebooks-Course/blob/main/img/Martra_Figure_4_2SDL_Tests.jpg?raw=true\">\n",
        "\n",
        "In the image below you can see the comparision between two tests.\n",
        "<img src=\"https://github.com/peremartra/Large-Language-Model-Notebooks-Course/blob/main/img/Martra_Figure_4_2SDL_CompareTestst.jpg?raw=true\">"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Well, since it has been so straightforward, why don't we try to make the comparison with an OpenAI model?"
      ],
      "metadata": {
        "id": "Xn6jeX7u_4MR"
      },
      "id": "Xn6jeX7u_4MR"
    },
    {
      "cell_type": "code",
      "source": [
        "if not 'OPENAI_API_KEY' in os.environ:\n",
        "  os.environ[\"OPENAI_API_KEY\"] = getpass(\"OPENAI API Key: \")"
      ],
      "metadata": {
        "id": "z4sYgAASO7_k"
      },
      "id": "z4sYgAASO7_k",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "84a97ac1-623e-4d28-a78b-c807af89c918",
      "metadata": {
        "id": "84a97ac1-623e-4d28-a78b-c807af89c918"
      },
      "outputs": [],
      "source": [
        "from langchain_openai import OpenAI\n",
        "open_aillm=OpenAI(temperature=0.0)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "project_name = f\"OpenAI {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\"\n",
        "\n",
        "finetuned_t5_results = run_on_dataset(\n",
        "    client=client,\n",
        "    project_name=project_name,\n",
        "    dataset_name=NAME_DATASET,\n",
        "    llm_or_chain_factory=open_aillm,\n",
        "    evaluation=evaluation_config,\n",
        ")"
      ],
      "metadata": {
        "id": "suc0G-9W94Qj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5587d6f3-a833-4553-81f4-aef01c018fa3"
      },
      "id": "suc0G-9W94Qj",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "View the evaluation results for project 'OpenAI 2024-07-28 13:57:52' at:\n",
            "https://smith.langchain.com/o/c67fe7fc-f385-519d-9a10-9464d2be3d9d/datasets/951ef736-0cb1-4d58-a4e2-d41da4d7b758/compare?selectedSessions=17493e77-b9f1-4f3d-9d23-79c99bcc83fc\n",
            "\n",
            "View all tests for Dataset Summarize_dataset_2024-07-28 13:57:02 at:\n",
            "https://smith.langchain.com/o/c67fe7fc-f385-519d-9a10-9464d2be3d9d/datasets/951ef736-0cb1-4d58-a4e2-d41da4d7b758\n",
            "[------------------------------------------------->] 3/3"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<img src=\"https://github.com/peremartra/Large-Language-Model-Notebooks-Course/blob/main/img/Martra_Figure_4_2SDL_CompareOpenAI_HF.jpg?raw=true\">"
      ],
      "metadata": {
        "id": "B15V_-3pBvOK"
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    },
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      "cell_type": "markdown",
      "source": [
        "The experiment with the OpenAI model has yielded the best results. But, be aware! As we can see, there is a cost involved since we are using an API, and it needs to be paid for.\n",
        "\n",
        "Another crucial piece of information is that we can view performance data for the models. This data could also be useful for minimally evaluating our inference server."
      ],
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